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Carnegie Mellon Robotics Institute

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Abstract

In this paper, we present an approach for potential negative obstacle detection based on missing data interpretation that extends traditional techniques driven by data only which capture the occupancy of the scene. The approach is decomposed into three steps: three-dimensional (3-D) data accumulation and low level classification, 3-D occluder propagation, and context-based occlusion labeling. The approach is validated using logged laser data collected in various outdoor natural terrains and also demonstrated live on-board the Demo-III eXperimental Unmanned Vehicle (XUV).

Keywords

negative obstacle, mobility, terrain classification, autonomous robot

Notes

Sponsor: Army Research Laboratory

Grant ID: DAAD19-01-209912

Associated Center(s) / Consortia: Vision and Autonomous Systems Center and Field Robotics Center

Associated Project(s): CTA Robotics

Text Reference

Nicholas Heckman, Jean-Francois Lalonde, Nicolas Vandapel, and Martial Hebert, "Potential Negative Obstacle Detection by Occlusion Labeling," IEEE/RSJ International Conference on Intelligent Robots and Systems, October, 2007.

BibTeX Reference

@inproceedings{Lalonde_2007_6073,
   author = "Nicholas Heckman and Jean-Francois Lalonde and Nicolas Vandapel and Martial Hebert",
   title = "Potential Negative Obstacle Detection by Occlusion Labeling",
   booktitle = "IEEE/RSJ International Conference on Intelligent Robots and Systems",
   month = "October",
   year = "2007",
}

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